Wavelet transform and cyclic cumulant based modulation classification in wireless network

Abstract

With the development of Internet of things, a large number of embedded devices are interconnected by ad hoc and wireless network. The embedded devices can work correctly, only by ensuring correct communication between them. Identifying modulation scheme is the precondition to ensure the correct communication between embedded devices. However, in the multipath channel, ensuring the correct communication between embedded devices is a great challenge. Multipath channel always exists in the wireless network. However, most of the available modulation classification algorithms are based on ideal channel. It leads to the low-modulation classification probability in multipath channel. To resolve this problem, we propose a novel modulation classification algorithm. The proposed algorithm can classify signal without prior information about multipath channel. We calculate feature by high-order cyclic cumulant and wavelet transform. The feature is robust to multipath channel. The simulation results show that the proposed algorithm can achieve the much better classification accuracy than the available method in multipath channel.

Automatic Detection of Modulation Scheme Using Convolutional Neural Networks

“…However they are only applicable to low level modulation schemes. Frequency domain mode figure out the energy distribution of signal as a feature, eradicated by way of DFT [18] [19], wavelet transforms [20] [21] or Wigner-Vile distribution [16] [22]. Merely their performance is degraded at low SNRs.…”

Radar Signals Modulation Recognition Based on Bispectrum Feature Processing

“…In reference [17][18][19][20], timefrequency image of signal is extracted by using smooth pseudo-Wigner time-frequency analysis, but the time-frequency image is easily affected by noise. In literature [21][22][23], wavelet transform is used to extract signal features, but how to select the appropriate wavelet function is a complicated problem. In literature [24], the signals bispectrum that is less affected by noise is taken as the feature of signal recognition to improve the anti-noise performance.…”